Reinforcement Learning-Driven Cluster Head Selection for Reliable Data Transmission in Dense Wireless Sensor Networks

Abstract: Wireless Sensor Networks (WSNs) have made significant advances towards practical applications. Data gathering in WSNs has been carried out using various techniques, such as multi-path routing, tree topologies, and clustering. Conventional systems lack a reliable and effective mechanism for dealing with end-to-end connection, traffic, and mobility problems. These deficiencies often lead to poor network performance. We propose an Internet of Things (IoT)-integrated densely distributed WSN system. The system utilizes a tree-based clustering approach dependent on the installed sensors' density. The cluster head nodes are structured in a tree-based cluster to optimize the process of gathering data. Each cluster's most efficient aggregation node is selected using a fuzzy inference-based reinforcement learning technique. The decision is based on three crucial factors: algebraic connectedness, bipartivity index, and neighborhood overlap. The proposed method significantly enhances energy efficiency and outperforms existing methods in bit error rate, throughput, packet delivery ratio, and delay.

Longyang Du, Qingxuan Wang and Zhigang ZHANG. “Reinforcement Learning-Driven Cluster Head Selection for Reliable Data Transmission in Dense Wireless Sensor Networks”. International Journal of Advanced Computer Science and Applications (IJACSA) 16.4 (2025). http://dx.doi.org/10.14569/IJACSA.2025.0160444

@article{Du2025,
title = {Reinforcement Learning-Driven Cluster Head Selection for Reliable Data Transmission in Dense Wireless Sensor Networks},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2025.0160444},
url = {http://dx.doi.org/10.14569/IJACSA.2025.0160444},
year = {2025},
publisher = {The Science and Information Organization},
volume = {16},
number = {4},
author = {Longyang Du and Qingxuan Wang and Zhigang ZHANG}
}